Overview

Dataset statistics

Number of variables15
Number of observations14458
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory120.0 B

Variable types

Numeric12
Categorical3

Alerts

TIME is highly correlated with S and 13 other fieldsHigh correlation
S is highly correlated with TIME and 13 other fieldsHigh correlation
T1 is highly correlated with TIME and 13 other fieldsHigh correlation
T4 is highly correlated with TIME and 13 other fieldsHigh correlation
T5 is highly correlated with TIME and 13 other fieldsHigh correlation
T6 is highly correlated with TIME and 13 other fieldsHigh correlation
T7 is highly correlated with TIME and 13 other fieldsHigh correlation
T9 is highly correlated with TIME and 13 other fieldsHigh correlation
T10 is highly correlated with TIME and 13 other fieldsHigh correlation
T11 is highly correlated with TIME and 13 other fieldsHigh correlation
T12 is highly correlated with TIME and 13 other fieldsHigh correlation
Z is highly correlated with TIME and 13 other fieldsHigh correlation
T2 is highly correlated with TIME and 13 other fieldsHigh correlation
T3 is highly correlated with TIME and 13 other fieldsHigh correlation
T8 is highly correlated with TIME and 13 other fieldsHigh correlation
TIME is uniformly distributed Uniform
TIME has unique values Unique
S has 5097 (35.3%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:27:41.072068
Analysis finished2022-11-11 03:27:49.424008
Duration8.35 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

TIME
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct14458
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean602.375
Minimum0
Maximum1204.75
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:49.450645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60.2375
Q1301.1875
median602.375
Q3903.5625
95-th percentile1144.5125
Maximum1204.75
Range1204.75
Interquartile range (IQR)602.375

Descriptive statistics

Standard deviation347.8174526
Coefficient of variation (CV)0.5774101724
Kurtosis-1.2
Mean602.375
Median Absolute Deviation (MAD)301.2083333
Skewness1.003477988 × 10-16
Sum8709137.75
Variance120976.9803
MonotonicityStrictly increasing
2022-11-11T11:27:49.510715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
800.08333331
 
< 0.1%
802.58333331
 
< 0.1%
802.66666671
 
< 0.1%
802.751
 
< 0.1%
802.83333331
 
< 0.1%
802.91666671
 
< 0.1%
8031
 
< 0.1%
803.08333331
 
< 0.1%
803.16666671
 
< 0.1%
Other values (14448)14448
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
ValueCountFrequency (%)
1204.751
< 0.1%
1204.6666671
< 0.1%
1204.5833331
< 0.1%
1204.51
< 0.1%
1204.4166671
< 0.1%
1204.3333331
< 0.1%
1204.251
< 0.1%
1204.1666671
< 0.1%
1204.0833331
< 0.1%
12041
< 0.1%

S
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7364.928413
Minimum0
Maximum20001
Zeros5097
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:49.568351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6000
Q314000
95-th percentile18000
Maximum20001
Range20001
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation6932.233711
Coefficient of variation (CV)0.9412492996
Kurtosis-1.371286945
Mean7364.928413
Median Absolute Deviation (MAD)6000
Skewness0.325572542
Sum106482135
Variance48055864.22
MonotonicityNot monotonic
2022-11-11T11:27:49.618469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
05097
35.3%
159991438
 
9.9%
140001200
 
8.3%
80001113
 
7.7%
12001719
 
5.0%
6000719
 
5.0%
9998719
 
5.0%
18000700
 
4.8%
3999685
 
4.7%
20001563
 
3.9%
Other values (23)1505
 
10.4%
ValueCountFrequency (%)
05097
35.3%
61
 
< 0.1%
91
 
< 0.1%
251
 
< 0.1%
471
 
< 0.1%
1101
 
< 0.1%
15191
 
< 0.1%
1998160
 
1.1%
2000559
 
3.9%
33441
 
< 0.1%
ValueCountFrequency (%)
20001563
 
3.9%
19999156
 
1.1%
18000700
4.8%
1799818
 
0.1%
171501
 
< 0.1%
159991438
9.9%
148271
 
< 0.1%
140001200
8.3%
13999237
 
1.6%
139681
 
< 0.1%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct51
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.46821483
Minimum24.7
Maximum27.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:49.675067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.7
5-th percentile24.8
Q124.9
median25.2
Q326
95-th percentile26.7
Maximum27.2
Range2.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.684685873
Coefficient of variation (CV)0.02688393661
Kurtosis-0.2545791466
Mean25.46821483
Median Absolute Deviation (MAD)0.4
Skewness0.8900117345
Sum368219.45
Variance0.4687947447
MonotonicityNot monotonic
2022-11-11T11:27:49.794490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.82368
16.4%
24.92246
15.5%
251181
 
8.2%
25.1960
 
6.6%
25.5856
 
5.9%
26627
 
4.3%
26.4605
 
4.2%
26.1564
 
3.9%
26.7553
 
3.8%
25.8543
 
3.8%
Other values (41)3955
27.4%
ValueCountFrequency (%)
24.7270
 
1.9%
24.751
 
< 0.1%
24.82368
16.4%
24.8520
 
0.1%
24.92246
15.5%
24.9524
 
0.2%
251181
8.2%
25.0522
 
0.2%
25.1960
6.6%
25.1520
 
0.1%
ValueCountFrequency (%)
27.2475
3.3%
27.152
 
< 0.1%
27.1111
 
0.8%
27.052
 
< 0.1%
2740
 
0.3%
26.951
 
< 0.1%
26.931
 
0.2%
26.851
 
< 0.1%
26.86
 
< 0.1%
26.752
 
< 0.1%

T2
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.1 KiB
24.7
7992 
24.8
3123 
24.6
1387 
24.9
1266 
24.5
 
690

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters57832
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.7
2nd row24.7
3rd row24.7
4th row24.7
5th row24.7

Common Values

ValueCountFrequency (%)
24.77992
55.3%
24.83123
 
21.6%
24.61387
 
9.6%
24.91266
 
8.8%
24.5690
 
4.8%

Length

2022-11-11T11:27:49.845339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:27:49.894983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.77992
55.3%
24.83123
 
21.6%
24.61387
 
9.6%
24.91266
 
8.8%
24.5690
 
4.8%

Most occurring characters

ValueCountFrequency (%)
214458
25.0%
414458
25.0%
.14458
25.0%
77992
13.8%
83123
 
5.4%
61387
 
2.4%
91266
 
2.2%
5690
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number43374
75.0%
Other Punctuation14458
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
214458
33.3%
414458
33.3%
77992
18.4%
83123
 
7.2%
61387
 
3.2%
91266
 
2.9%
5690
 
1.6%
Other Punctuation
ValueCountFrequency (%)
.14458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common57832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
214458
25.0%
414458
25.0%
.14458
25.0%
77992
13.8%
83123
 
5.4%
61387
 
2.4%
91266
 
2.2%
5690
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII57832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
214458
25.0%
414458
25.0%
.14458
25.0%
77992
13.8%
83123
 
5.4%
61387
 
2.4%
91266
 
2.2%
5690
 
1.2%

T3
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.1 KiB
24.7
6284 
24.8
4664 
24.6
2104 
24.5
711 
24.4
695 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters57832
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.8
2nd row24.8
3rd row24.8
4th row24.8
5th row24.8

Common Values

ValueCountFrequency (%)
24.76284
43.5%
24.84664
32.3%
24.62104
 
14.6%
24.5711
 
4.9%
24.4695
 
4.8%

Length

2022-11-11T11:27:49.942040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:27:49.991282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.76284
43.5%
24.84664
32.3%
24.62104
 
14.6%
24.5711
 
4.9%
24.4695
 
4.8%

Most occurring characters

ValueCountFrequency (%)
415153
26.2%
214458
25.0%
.14458
25.0%
76284
10.9%
84664
 
8.1%
62104
 
3.6%
5711
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number43374
75.0%
Other Punctuation14458
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
415153
34.9%
214458
33.3%
76284
14.5%
84664
 
10.8%
62104
 
4.9%
5711
 
1.6%
Other Punctuation
ValueCountFrequency (%)
.14458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common57832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
415153
26.2%
214458
25.0%
.14458
25.0%
76284
10.9%
84664
 
8.1%
62104
 
3.6%
5711
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII57832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
415153
26.2%
214458
25.0%
.14458
25.0%
76284
10.9%
84664
 
8.1%
62104
 
3.6%
5711
 
1.2%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.98234887
Minimum24.6
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.033799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.6
5-th percentile24.6
Q124.7
median25
Q325.2
95-th percentile25.3
Maximum25.5
Range0.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2407901602
Coefficient of variation (CV)0.009638411561
Kurtosis-1.128902992
Mean24.98234887
Median Absolute Deviation (MAD)0.2
Skewness-0.1524440832
Sum361194.8
Variance0.05797990124
MonotonicityNot monotonic
2022-11-11T11:27:50.072050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
25.22622
18.1%
25.12484
17.2%
24.72160
14.9%
251811
12.5%
24.61660
11.5%
25.31218
8.4%
24.91037
 
7.2%
24.8967
 
6.7%
25.4338
 
2.3%
25.5161
 
1.1%
ValueCountFrequency (%)
24.61660
11.5%
24.72160
14.9%
24.8967
 
6.7%
24.91037
 
7.2%
251811
12.5%
25.12484
17.2%
25.22622
18.1%
25.31218
8.4%
25.4338
 
2.3%
25.5161
 
1.1%
ValueCountFrequency (%)
25.5161
 
1.1%
25.4338
 
2.3%
25.31218
8.4%
25.22622
18.1%
25.12484
17.2%
251811
12.5%
24.91037
 
7.2%
24.8967
 
6.7%
24.72160
14.9%
24.61660
11.5%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.0445359
Minimum24.8
Maximum25.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.111220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.8
5-th percentile24.8
Q124.8
median25
Q325.3
95-th percentile25.4
Maximum25.4
Range0.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2138901657
Coefficient of variation (CV)0.008540392465
Kurtosis-1.444828368
Mean25.0445359
Median Absolute Deviation (MAD)0.2
Skewness0.2055811437
Sum362093.9
Variance0.04574900297
MonotonicityNot monotonic
2022-11-11T11:27:50.148368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
24.84669
32.3%
25.32997
20.7%
252109
14.6%
25.11567
 
10.8%
25.21055
 
7.3%
25.41034
 
7.2%
24.91027
 
7.1%
ValueCountFrequency (%)
24.84669
32.3%
24.91027
 
7.1%
252109
14.6%
25.11567
 
10.8%
25.21055
 
7.3%
25.32997
20.7%
25.41034
 
7.2%
ValueCountFrequency (%)
25.41034
 
7.2%
25.32997
20.7%
25.21055
 
7.3%
25.11567
 
10.8%
252109
14.6%
24.91027
 
7.1%
24.84669
32.3%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.9175543
Minimum24.6
Maximum25.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.188354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.6
5-th percentile24.6
Q124.7
median24.9
Q325.1
95-th percentile25.3
Maximum25.3
Range0.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2040376687
Coefficient of variation (CV)0.008188511052
Kurtosis-0.9668382218
Mean24.9175543
Median Absolute Deviation (MAD)0.2
Skewness0.2932079583
Sum360258
Variance0.04163137026
MonotonicityNot monotonic
2022-11-11T11:27:50.226320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
24.72640
18.3%
24.82635
18.2%
252200
15.2%
24.91939
13.4%
25.11569
10.9%
25.21309
9.1%
25.31098
7.6%
24.61068
7.4%
ValueCountFrequency (%)
24.61068
7.4%
24.72640
18.3%
24.82635
18.2%
24.91939
13.4%
252200
15.2%
25.11569
10.9%
25.21309
9.1%
25.31098
7.6%
ValueCountFrequency (%)
25.31098
7.6%
25.21309
9.1%
25.11569
10.9%
252200
15.2%
24.91939
13.4%
24.82635
18.2%
24.72640
18.3%
24.61068
7.4%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.65045649
Minimum24.3
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.267535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.3
5-th percentile24.4
Q124.6
median24.7
Q324.7
95-th percentile24.8
Maximum24.8
Range0.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1276424339
Coefficient of variation (CV)0.005178096152
Kurtosis0.8056637847
Mean24.65045649
Median Absolute Deviation (MAD)0.1
Skewness-1.141082456
Sum356396.3
Variance0.01629259093
MonotonicityNot monotonic
2022-11-11T11:27:50.309438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
24.76791
47.0%
24.82611
 
18.1%
24.62328
 
16.1%
24.51389
 
9.6%
24.4688
 
4.8%
24.3651
 
4.5%
ValueCountFrequency (%)
24.3651
 
4.5%
24.4688
 
4.8%
24.51389
 
9.6%
24.62328
 
16.1%
24.76791
47.0%
24.82611
 
18.1%
ValueCountFrequency (%)
24.82611
 
18.1%
24.76791
47.0%
24.62328
 
16.1%
24.51389
 
9.6%
24.4688
 
4.8%
24.3651
 
4.5%

T8
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.1 KiB
24.7
5004 
24.8
4698 
24.6
3370 
24.9
1386 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters57832
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.6
2nd row24.6
3rd row24.6
4th row24.6
5th row24.6

Common Values

ValueCountFrequency (%)
24.75004
34.6%
24.84698
32.5%
24.63370
23.3%
24.91386
 
9.6%

Length

2022-11-11T11:27:50.354322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:27:50.402406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.75004
34.6%
24.84698
32.5%
24.63370
23.3%
24.91386
 
9.6%

Most occurring characters

ValueCountFrequency (%)
214458
25.0%
414458
25.0%
.14458
25.0%
75004
 
8.7%
84698
 
8.1%
63370
 
5.8%
91386
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number43374
75.0%
Other Punctuation14458
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
214458
33.3%
414458
33.3%
75004
 
11.5%
84698
 
10.8%
63370
 
7.8%
91386
 
3.2%
Other Punctuation
ValueCountFrequency (%)
.14458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common57832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
214458
25.0%
414458
25.0%
.14458
25.0%
75004
 
8.7%
84698
 
8.1%
63370
 
5.8%
91386
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII57832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
214458
25.0%
414458
25.0%
.14458
25.0%
75004
 
8.7%
84698
 
8.1%
63370
 
5.8%
91386
 
2.4%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.66266427
Minimum25.3
Maximum29.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.454466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.3
5-th percentile25.5
Q126.9
median28.15
Q328.6
95-th percentile28.9
Maximum29.3
Range4
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.205836954
Coefficient of variation (CV)0.04359077427
Kurtosis-0.8463567792
Mean27.66266427
Median Absolute Deviation (MAD)0.55
Skewness-0.8403044435
Sum399946.8
Variance1.454042759
MonotonicityNot monotonic
2022-11-11T11:27:50.512000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.51106
 
7.6%
28.6932
 
6.4%
28.7845
 
5.8%
28.4801
 
5.5%
28.3747
 
5.2%
25.8670
 
4.6%
28.8644
 
4.5%
25.5610
 
4.2%
27.5592
 
4.1%
28.9532
 
3.7%
Other values (71)6979
48.3%
ValueCountFrequency (%)
25.384
 
0.6%
25.3515
 
0.1%
25.4470
3.3%
25.4520
 
0.1%
25.5610
4.2%
25.5519
 
0.1%
25.6370
2.6%
25.6512
 
0.1%
25.7348
2.4%
25.7529
 
0.2%
ValueCountFrequency (%)
29.329
 
0.2%
29.254
 
< 0.1%
29.270
 
0.5%
29.158
 
0.1%
29.1207
 
1.4%
29.0525
 
0.2%
29275
1.9%
28.9527
 
0.2%
28.9532
3.7%
28.8527
 
0.2%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.80609351
Minimum24.3
Maximum25.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.588331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.3
5-th percentile24.4
Q124.6
median24.9
Q325
95-th percentile25.1
Maximum25.3
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2279758002
Coefficient of variation (CV)0.00919031447
Kurtosis-0.871427661
Mean24.80609351
Median Absolute Deviation (MAD)0.1
Skewness-0.2648418142
Sum358646.5
Variance0.05197296545
MonotonicityNot monotonic
2022-11-11T11:27:50.628338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
24.93123
21.6%
252357
16.3%
24.81939
13.4%
24.51886
13.0%
24.61664
11.5%
25.11346
9.3%
24.7686
 
4.7%
24.4679
 
4.7%
25.2456
 
3.2%
24.3204
 
1.4%
ValueCountFrequency (%)
24.3204
 
1.4%
24.4679
 
4.7%
24.51886
13.0%
24.61664
11.5%
24.7686
 
4.7%
24.81939
13.4%
24.93123
21.6%
252357
16.3%
25.11346
9.3%
25.2456
 
3.2%
ValueCountFrequency (%)
25.3118
 
0.8%
25.2456
 
3.2%
25.11346
9.3%
252357
16.3%
24.93123
21.6%
24.81939
13.4%
24.7686
 
4.7%
24.61664
11.5%
24.51886
13.0%
24.4679
 
4.7%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.9544612
Minimum23.9
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.672200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.9
5-th percentile24.1
Q124.5
median25.1
Q325.3
95-th percentile25.6
Maximum25.9
Range2
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.4965494308
Coefficient of variation (CV)0.01989822288
Kurtosis-0.9759190369
Mean24.9544612
Median Absolute Deviation (MAD)0.3
Skewness-0.4368253515
Sum360791.6
Variance0.2465613373
MonotonicityNot monotonic
2022-11-11T11:27:50.718677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
25.21802
12.5%
25.31514
10.5%
25.11499
10.4%
24.21423
9.8%
251350
9.3%
25.5985
 
6.8%
24.3928
 
6.4%
25.4872
 
6.0%
25.6617
 
4.3%
24.1593
 
4.1%
Other values (11)2875
19.9%
ValueCountFrequency (%)
23.988
 
0.6%
24160
 
1.1%
24.1593
4.1%
24.21423
9.8%
24.3928
6.4%
24.4394
 
2.7%
24.5230
 
1.6%
24.6420
 
2.9%
24.7274
 
1.9%
24.8195
 
1.3%
ValueCountFrequency (%)
25.9142
 
1.0%
25.8165
 
1.1%
25.7257
 
1.8%
25.6617
 
4.3%
25.5985
6.8%
25.4872
6.0%
25.31514
10.5%
25.21802
12.5%
25.11499
10.4%
251350
9.3%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.85858348
Minimum23.6
Maximum25.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.770195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.6
5-th percentile24
Q124.4
median25
Q325.2
95-th percentile25.4
Maximum25.8
Range2.2
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.4979003983
Coefficient of variation (CV)0.02002931497
Kurtosis-0.9385053007
Mean24.85858348
Median Absolute Deviation (MAD)0.3
Skewness-0.6138731419
Sum359405.4
Variance0.2479048066
MonotonicityNot monotonic
2022-11-11T11:27:50.876094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
25.12053
14.2%
25.21918
13.3%
251523
10.5%
24.11429
9.9%
25.41249
8.6%
25.31115
7.7%
24.91100
7.6%
24.2961
6.6%
24698
 
4.8%
25.5372
 
2.6%
Other values (13)2040
14.1%
ValueCountFrequency (%)
23.630
 
0.2%
23.769
 
0.5%
23.820
 
0.1%
23.9175
 
1.2%
24698
4.8%
24.11429
9.9%
24.2961
6.6%
24.3214
 
1.5%
24.4285
 
2.0%
24.5290
 
2.0%
ValueCountFrequency (%)
25.825
 
0.2%
25.7152
 
1.1%
25.6146
 
1.0%
25.5372
 
2.6%
25.41249
8.6%
25.31115
7.7%
25.21918
13.3%
25.12053
14.2%
251523
10.5%
24.91100
7.6%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct150
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.74292807
Minimum0
Maximum82.875
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size113.1 KiB
2022-11-11T11:27:50.929997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.406
Q137.781
median48.75
Q358.5
95-th percentile73.125
Maximum82.875
Range82.875
Interquartile range (IQR)20.719

Descriptive statistics

Standard deviation16.38304933
Coefficient of variation (CV)0.3431513313
Kurtosis0.1178391531
Mean47.74292807
Median Absolute Deviation (MAD)9.75
Skewness-0.3825185732
Sum690267.254
Variance268.4043052
MonotonicityNot monotonic
2022-11-11T11:27:50.984806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.75699
 
4.8%
47.531649
 
4.5%
45.093522
 
3.6%
46.312496
 
3.4%
43.875475
 
3.3%
52.406444
 
3.1%
35.343415
 
2.9%
64.593391
 
2.7%
53.625387
 
2.7%
31.687357
 
2.5%
Other values (140)9623
66.6%
ValueCountFrequency (%)
015
 
0.1%
1.21851
 
< 0.1%
4.87451
 
< 0.1%
6.70252
 
< 0.1%
7.31246
0.3%
7.921515
 
0.1%
8.53150
0.3%
9.14052
 
< 0.1%
9.7535
0.2%
10.35926
0.2%
ValueCountFrequency (%)
82.87512
 
0.1%
82.265511
 
0.1%
81.656213
1.5%
81.046585
 
0.6%
80.437144
1.0%
79.827522
 
0.2%
79.21842
 
0.3%
78.6098
 
0.1%
7842
 
0.3%
77.39051
 
< 0.1%

Interactions

2022-11-11T11:27:48.676817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:41.792659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.429644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.044700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.688379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.256472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.919195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.528639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.174200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.788478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.422964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.042832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.723659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:41.843487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.482787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.091685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.735941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.305685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.969185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.576549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.224079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.835391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.474294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.089674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.772936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:41.897356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.536545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.141854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.785391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.358912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.023252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.627429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.279284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.885735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.529732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.140503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.817571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:41.943913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.584430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.186268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.830240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.406832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.072087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.673274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.328120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.931580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.578620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.185352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.861818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.035762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.633233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.232219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.876086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.453721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.119979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.720116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.377019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.976429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.628369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.230201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.910237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.085648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.685005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.355748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.923416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.503901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.171141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.769008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.428679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.024990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.680902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.278039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.959262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.136883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.738898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.405404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.973612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.555531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.223963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.819971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.482499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.074311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.734721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.327744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:49.005142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.185719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.789131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.451249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.019543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.670111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.273795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.866814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.532381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.121197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.785795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.374527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:49.055076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.238588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.844123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.503931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.070788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.723023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.327382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.983898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.587378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.173091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.840551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.426201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:49.100734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.285362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.892981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.547835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.115637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.770160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.375875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.031285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.636214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.281691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.890328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.472052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:49.151217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.337897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.948022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.598631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.167543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.824514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.430736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.083158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.691786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.333174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.945143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.588394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:49.195502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.384665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:42.996857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:43.644476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.212620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:44.872353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:45.479806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.129351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:46.740639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.378147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:47.993997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:48.633094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:27:51.039039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:27:51.113367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:27:51.190641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:27:51.268005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:27:51.337274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-11T11:27:51.391011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:27:49.272185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:27:49.381139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
00.000000024.724.724.824.724.925.324.724.625.5524.323.923.60.0
10.083333024.724.724.824.724.925.324.724.625.5024.323.923.60.0
20.166667024.724.724.824.724.925.324.724.625.5024.323.923.60.0
30.250000024.724.724.824.724.925.324.724.625.5024.323.923.60.0
40.333333024.724.724.824.724.925.324.724.625.5024.323.923.60.0
50.416667024.724.724.824.724.925.324.724.625.5024.323.923.60.0
60.500000024.724.724.824.724.925.324.724.625.5024.323.923.60.0
70.583333024.724.724.824.724.925.324.724.625.5024.323.923.60.0
80.666667024.724.724.824.724.925.324.724.625.5024.323.923.60.0
90.750000024.724.724.824.724.925.324.724.625.5024.323.923.60.0

Last rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
144481204.000000024.824.924.825.125.425.324.824.827.525.025.025.029.25
144491204.083333024.824.924.825.125.425.324.824.827.525.025.025.029.25
144501204.166667024.824.924.825.125.425.324.824.827.525.025.025.029.25
144511204.250000024.824.924.825.125.425.324.824.827.525.025.025.029.25
144521204.333333024.824.924.825.125.425.324.824.827.525.025.025.129.25
144531204.416667024.824.924.825.125.425.324.824.827.525.025.025.129.25
144541204.500000024.824.924.825.125.425.324.824.827.525.025.025.129.25
144551204.583333024.824.924.825.125.425.324.824.827.525.025.025.029.25
144561204.666667024.824.924.825.125.425.324.824.827.525.025.025.129.25
144571204.750000024.824.924.825.125.425.324.824.827.525.025.025.129.25